VMware vSphere Bitfusion virtualizes hardware accelerators such as graphical processing units (GPUs) to provide a pool of shared, network-accessible resources that support artificial intelligence (AI) and machine learning (ML) workloads. vSphere Bitfusion works with artificial intelligence frameworks such as TensorFow and PyTorch. You can deploy vSphere Bitfusion within a virtual machine or Docker container for use in data center environments. With vSphere Bitfusion, you can monitor health, utilization, efficiency, and availability of all GPU servers in the network. You can also monitor client consumption of GPUs and assign quotas and time limits.

vSphere Diagram illustrating how vSphere Bitfusion extends GPU virtualization.

Want to know what is in the current release of vSphere Bitfusion? Look at the latest vSphere Bitfusion release notes.

Learn About Some of Our vSphere Bitfusion Features

Learn the basic concepts of vSphere Bitfusion, and how it virtualizes GPUs and provides a pool of shared compute resources for use by AI and ML applications.

Learn how to install the vSphere Bitfusion server and client in your vSphere environment, including the software and hardware requirements necessary to run vSphere Bitfusion.

You can use a Paravirtual RDMA (PVRDMA) adapter to improve the performance of your vSphere Bitfusion deployment. RDMA allows applications direct access to the memory from one computer to the memory of another computer without involving the operating system or CPU.

You can upgrade a vSphere Bitfusion Cluster without losing your current cluster configuration and monitoring data.

Learn how to start and stop vSphere Bitfusion applications, and how to allocate GPUs to run multiple applications on the same GPUs.

Learn how to monitor vSphere Bitfusion using the graphical user interface provided by the vSphere Bitfusion Plug-in within the vSphere Client. You can view current and historical statistics of GPU allocation and usage, memory usage, network traffic statistics, and other data of your vSphere Bitfusion servers and clients. You can also export and download monitoring data as a .csv file to review and troubleshoot your vSphere Bitfusion environment.

You can check the performance, stability, available system resources, and software version of a vSphere Bitfusion server by performing a health check. You can also troubleshoot your vSphere Bitfusion environment by examining log files specific to the vSphere Bitfusion server.

Learn how to backup and restore the vSphere Bitfusion database. By backing up the database, you can save a snapshot of the configuration, connectivity, health state, and history of your vSphere Bitfusion cluster data. If there is a failure, you can restore the vSphere Bitfusion database and recover the cluster using the snapshot.

Learn how to install and use TensorFlow, and run benchmarks to test the performance of your vSphere Bitfusion and TensorFlow deployment. To use TensorFlow, you also install NVIDIA CUDA and NVIDIA CUDA Deep Neural Network library (cuDNN). CUDA is a computing library developed by NVIDIA that enables general computing on GPUs. cuDNN is a GPU-accelerated library of primitives for use with deep neural networks.

Download vSphere Bitfusion

Download the vSphere Bitfusion appliance and client software packages to begin your deployment.

Explore Our Videos

You can learn about deploying and operating vSphere Bitfusion by reading the documentation, and by watching videos on the VMware vSphere YouTube channel.

Learn More About vSphere Bitfusion

To learn about vSphere Bitfusion and GPU virtualization, see the following resources.
  • Learn more about vSphere Bitfusion by visiting vSphere Bitfusion Solutions.
  • Learn about TensorFlow, an end-to-end open-source platform for machine learning. TensorFlow makes it easy to create machine learning models for desktop, mobile, web, and cloud environments.
  • vSphere Bitfusion integrates with CUDA, a parallel computing platform developed by NVIDIA for general computing on GPUs. With CUDA, you can dramatically speed up computing applications by harnessing the power of GPUs. Applications developed with CUDA have been deployed to GPUs in embedded systems, workstations, data centers, and in the cloud.
  • Understand how NVIDIA cuDNN, a GPU-accelerated library of primitives for use with deep neural networks, integrates with vSphere Bitfusion to accelerate the GPU performance. This integration allows you to focus on training neural networks and developing software applications rather than spending time on low-level GPU performance tuning.

Use vSphere Bitfusion Documentation

The vSphere Bitfusion documents in HTML reflect the latest update release of each major vSphere Bitfusion version. For example, version 2.5 contains all the updates for 2.5.x releases.

You can create custom documentation collections, containing only the content that meets your specific information needs, using MyLibrary.